Improved ANN-Based Approach Using Relative Impact for the Prediction of Thermal Coal Elemental Composition Using Proximate Analysis.

The basic properties of coal influence various procedures of power generation, such as the design of power generation plants, estimation of the current condition of boilers, and total efficiency of power plants. The elemental composition is a needed factor in evaluating the process of chemical conversion and predicting the flow of flue gas and the quality of air in coal combustion. In the past, several relationships have been established using ultimate and proximate analyses. This study aims to predict the elemental compositions of 104 thermal coals used for coal-fired power plants in South Korea using an artificial neural network (ANN) that uses proximate analysis values as input parameters. The ANN-based model was optimized with six activation functions and four hidden layers after evaluating various performance indices, including R 2, mean square error (MSE), and epoch, then additional calculations were derived to compare performances from previous research using the mean absolute error (MAE), average absolute error, and average bias error. It was found that the best topology was established using the Levenberg-Marquardt activation function and 10 hidden layers, resulting in the highest R 2 value and smallest MSE of all topologies tested. As a result, the relative impact on calculation accuracy was derived from ANN hidden layers to analyze prediction accuracies of carbon, hydrogen, and oxygen compositions. Accuracy was improved over previous results by 4.71-0.91% via coal rank division topology optimization. Based on the MAE, the current results are even close in performance to those of adaptive neuro-fuzzy inference systems. They also outperformed previous research models by 5.40 and 7.39% in terms of MAE accuracy. Applicability of the ANN was also analyzed with limitations of the chemical composition of ANNs and present reinforcement measures in the future studies through qualitative analysis comparisons based on Fourier transform infrared spectroscopy. Consequently, the relative effect was derived from the ANN hidden layer weight for specific calculation of the relationship between elemental composition and proximate analysis properties. As a result, it was possible to qualitatively analyze how the proximate analysis value affects the composition of elements and calculate the ratio accordingly. The findings of this study provide an improved and efficient approach to predicting the elemental composition of thermal coal, based on data from South Korean power plants. Also, further research can follow schematics from this study with the applicability and accessibility of the ANN.

A novel statistical-dynamical method for a seasonal forecast of particular matter in South Korea.

Societal concerns about air quality in East Asia are still growing despite country-level efforts to reduce air pollution emissions. In coping with this growing concern, the government and the public demand a longer­lead forecast of air quality to ensure sufficient response time until society prepares for countermeasures such as a temporary reduction of specific emission sources. Here we propose a novel method that produces skillful seasonal forecasting of wintertime (December to February) PM10 concentration over South Korea. The method is based on the idea that climate condition and air quality have co-variability in the seasonal time scales and that the state-of-art seasonal prediction model will benefit air quality forecasting. More specifically, a linear regression model is constructed to link observed winter PM10 concentration and climate variables where the predicted climate variables were furnished from NCEP CFSv2 forecast initialized during autumn. In this case, climate variables were selected as predictors of the model because they are not only physically related to air quality but also 'predictable' in CFS hindcast. Through analysis of retrospective forecasts of 20 winters for the period 2001-2020, we found this model shows statistically significant skill for the seasonal forecast of wintertime PM10 concentration.

Interactive Videogame Improved Rehabilitation Motivation and Walking Speed in Chronic Stroke Patients: A Dual-Center Controlled Trial.

Objective: This study aimed to evaluate the effectiveness of interactive video games (IVGs) in rehabilitation motivation and walking and balance abilities in chronic stroke patients. Materials and Methods: In this dual-center controlled trial, 24 chronic stroke patients from rehabilitation centers A and B were randomly assigned to an experimental (IVGs + traditional neurodevelopment treatment [TNT], n = 12) or a control group (walking training + TNT, n = 12). The patients in both the groups underwent TNT for 4 weeks (5 days/week) before undergoing either IVGs or walking training (4 weeks, 3 days/week) depending on the group. The primary and secondary outcomes were rehabilitation motivation and the Berg Balance Scale (BBS) score, Functional Reach Test (FRT) performance, and walking speed (WS), respectively. Results: The patients in both the groups showed significant increase in the BBS score, FRT performance, and WS; however, the experimental group showed more significant improvements in rehabilitation motivation (P = 0.02, η2 = 0.415) and WS (P = 0.05, η2 = 0.333) than the patients in the control group. Conclusion: This study suggests that the IVGs in combination with TNT provide effective rehabilitation motivation in chronic stroke patients. Clinical Trial Registration number: KCT0003408.

Statistical Seasonal Forecasting of Winter and Spring PM2.5 Concentrations Over the Korean Peninsula.

Concentrations of fine particulate matter smaller than 2.5 µm in diameter (PM2.5) over the Korean Peninsula experience year-to-year variations due to interannual variation in climate conditions. This study develops a multiple linear regression model based on slowly varying boundary conditions to predict winter and spring PM2.5 concentrations at 1-3-month lead times. Nation-wide observations of Korea, which began in 2015, is extended back to 2005 using the local Seoul government's observations, constructing a long-term dataset covering the 2005-2019 period. Using the forward selection stepwise regression approach, we identify sea surface temperature (SST), soil moisture, and 2-m air temperature as predictors for the model, while rejecting sea ice concentration and snow depth due to weak correlations with seasonal PM2.5 concentrations. For the wintertime (December-January-February, DJF), the model based on SSTs over the equatorial Atlantic and soil moisture over the eastern Europe along with the linear PM2.5 concentration trend generates a 3-month forecasts that shows a 0.69 correlation with observations. For the springtime (March-April-May, MAM), the accuracy of the model using SSTs over North Pacific and 2-m air temperature over East Asia increases to 0.75. Additionally, we find a linear relationship between the seasonal mean PM2.5 concentration and an extreme metric, i.e., seasonal number of high PM2.5 concentration days. Supplementary Information: The online version contains supplementary material available at 10.1007/s13143-022-00275-4.

Low-Temperature Oxidation Reactivity of Low-Rank Coals and Their Petrographic Properties.

Through the oxidation of coal at low temperatures and the resulting petrographic analysis, this study aims to predict spontaneous combustion, which has emerged as an industrial problem. Low-temperature oxidation analysis and the corresponding petrographic characteristics of four different coals treated under low temperatures of 25, 50, and 75 °C, which was set as the reactor temperature, were investigated. Low-temperature oxidation experiments designed at Pusan National University, based on papers related to low-temperature experiments, were conducted to analyze the constant of oxidation reactions. The petrographic characteristics of the coals were analyzed using a coal petrographic microscope spectrophotometer for determining their vitrinite reflectance and morphology, and the coals were extracted after the low-temperature oxidation experiments. After these analyses, vitrinite reflectance changed, and the normalized k, which is the difference between the constant of reaction from 25 °C to (the setting temperatures of) 50 and 75 °C, was also calculated. By comparing the oxidation rates of the coals and the corresponding petrographic analyses, the cause of spontaneous combustion can be deduced and a prediction can be made about which coal burns most efficiently at a low temperature.

The Effectiveness of Driving Game on Trunk Control and Gait Ability in Stroke.

Patients who require neurological rehabilitation often do not comply with conventional programs because they find the therapy uninteresting. As a result, specialized interactive video games have been designed to be more enjoyable than conventional therapy (CT) tasks. This study aimed to assess the trunk control and gait ability of patients with chronic stroke after participation in driving-based interactive video games (DBIVG). Participants included 24 chronic stroke patients allocated to an experimental group (n = 13, CT + DBIVG) or a control group (n = 11, CT + treadmill walking training). Both groups received CT five days/week; the experimental and control groups participated in DBIVG and treadmill walking training, respectively, three days/week for four weeks. The primary outcome of trunk control was measured by the trunk impairment scale (TISall) and TIS subscales, including static sitting balance (TISssb), dynamic sitting balance (TISdsb), and trunk co-ordination (TISco). Gait ability was measured by the dynamic gait index (DGI), timed walking test (TWT), and time up and go test (TUGT). Both groups demonstrated significant improvements in TISall, TISdsb, and TUGT results. The experimental group showed significantly greater improvement in TISssb, TISco, and DGI than the control group. Our findings indicate that DBIVG can improve trunk control and gait ability in patients with chronic stroke.

Emission factors of air toxics from semiconductor manufacturing in Korea.

The development of local, accurate emission factors is very important for the estimation of reliable national emissions and air quality management. For that, this study is performed for pollutants released to the atmosphere with source-specific emission tests from the semiconductor manufacturing industry. The semiconductor manufacturing industry is one of the major sources of air toxics or hazardous air pollutants (HAPs); thus, understanding the emission characteristics of the emission source is a very important factor in the development of a control strategy. However, in Korea, there is a general lack of information available on air emissions from the semiconductor industry. The major emission sources of air toxics examined from the semiconductor manufacturing industry were wet chemical stations, coating applications, gaseous operations, photolithography, and miscellaneous devices in the wafer fabrication and semiconductor packaging processes. In this study, analyses of emission characteristics, and the estimations of emission data and factors for air toxics, such as acids, bases, heavy metals, and volatile organic compounds from the semiconductor manufacturing process have been performed. The concentration of hydrogen chloride from the packaging process was the highest among all of the processes. In addition, the emission factor of total volatile organic compounds (TVOCs) for the packaging process was higher than that of the wafer fabrication process. Emission factors estimated in this study were compared with those of Taiwan for evaluation, and they were found to be of similar level in the case of TVOCs and fluorine compounds.